Shiv O. Prasher

Removal of Selected Metal Ions from Aqueous Solutions Using Chitosan Flakes

Commercially available chitosans potential in the adsorption of heavy metals like zinc, copper, cadmium, and lead from aqueous solutions under variable physicochemical conditions was investigated. The results obtained from equilibrium and kinetic studies showed that there was significant uptake of these metal ions by chitosan and that chitosan flakes had a maximum sorption capacity for copper ions. The order of metal ion adsorption by chitosan decreased from Cu2+ to Zn2+ as follows: copper lead cadmium zinc. There was a considerable increase in sorption capacity with an increase in chitosan amount; however, this parallelism diminished when the chitosan mass exceeded 0.24 g in 25 mL of metal solution. The sorption of metal ions from various salt solutions by chitosan flakes was not improved by agitation. The heavy metal uptake by chitosan was found to be pH-dependent, with a maximum at pH 6.0 and 7.0. Sorption equilibrium studies were conducted with a constant sorbent weight and varying initial concentration of metal ions. The experimental data of adsorption from solutions containing metal ions were found to correlate well with the Langmuir isotherm equation.

Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn

This study evaluates the potential of decision tree classification algorithms for the classification of hyperspectral data, with the goal of discriminating between different growth scenarios in a cornfield. A comparison was also made between decision tree and artificial neural networks (ANNs) classification accuracies. In the summer of the year 2000, a two-factor field experiment representing different crop conditions was carried out. Corn was grown under four weed management strategies: no weed control, control of grasses, control of broadleaf weeds, and full weed control with nitrogen levels of 60, 120, and 250 N kg/ha. Hyperspectral data using a Compact Airborne Spectrographic Imager were acquired three times during the entire growing season. Decision tree technology was applied to classify different treatments based on the hyperspectral data. Various tree-growing mechanisms were used to improve the accuracy of classification. Misclassification rates of detecting all the combinations of different nitrogen and weed categories were 43, 32, and 40% for hyperspectral data sets obtained at the initial growth, the tasseling and the full maturity stages, respectively. However, satisfactory classification results were obtained when one factor (nitrogen or weed) was considered at a time. In this case, misclassification rates were only 22 and 18% for nitrogen and weeds, respectively, for the data obtained at the tasseling stage. Slightly better results were obtained by following the ANN approach. However, the advantage with the decision tree was the formulation of simple and clear classification rules. The highest accuracy was obtained for the data acquired at tasseling stage. The results indicate the potential of decision tree classification algorithms and ANN usage in the classification of hyperspectral data for crop condition assessment.

Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique.

Many subjective grading methods with poor repeatability and tedious procedures are still widely used in meat industry. In this study, a hyperspectral-imaging-based technique was investigated to evaluate its potentials for objective determination of pork quality attributes. The system extracted spectral and spatial characteristics simultaneously to determinate the quality attributes, drip loss, pH, and color, of pork meat. Six feature band images were selected for predicting the drip loss (459, 618, 655, 685, 755 and 953nm), pH (494, 571,637, 669, 703 and 978nm) and color (434, 494, 561, 637, 669 and 703nm), respectively. Two intensity indices of the band images were used as inputs to establish neural network models to predict the quality attributes. The results showed that with the hyperspectral-imaging system, the drip loss, pH, and color of pork meat could be predicted with correlation coefficients of 0.77, 0.55 and 0.86, respectively. Pork meat could be classified based on their exudative characteristics and color successfully.

The effect of composting on the degradation of a veterinary pharmaceutical

Composting has been identified as a viable means of reducing the environmental impact of antibiotics in manure. The focus of the present study is the potential use of composting on the degradation of salinomycin in manure prior to its field application. Manure contaminated with salinomycin was collected from a poultry farm and adjusted to a C:N ratio of 25:1 with hay material. The manure was composted in three identical 120 L plastic containers, 0.95 m height x 0.40 m in diameter. The degradation potential for salinomycin was also ascertained under open heap conditions for comparison (control). Salinomycin was quantified on HPLC with a Charged Aerosol Detector, at an interval of every 3 days. The salinomycin level in the compost treatment decreased from 22 mg kg(-1) to 2 x 10(-5) microg kg(-1) over 38 days. The corresponding decrease in the control was from 27.5 mg kg(-1) to 24 microg kg(-1). The changes in pH, EC (dS m(-1)), temperature, total kjeldahl nitrogen (TKN), total potassium (TK), total phosphorus (TP) and carbon content in both the composting and the control samples were monitored and found to be different in compost as compared to the control. During the composting process, the loss of TKN was 36%, which was substantially lower than corresponding loss of 60% in the control. The loss of carbon was 10% during composting, whereas the loss in the control was 2%. In composting, the temperature modulated from 27 degrees C (initially) to a high of 62.8 degrees C (after 4 days), and then declined to 27.8 degrees C at the end of 38 days. On the basis of the results obtained in this study, it appears that the composting technique is effective in reducing salinomycin in manure.

ESTIMATION OF CROP BIOPHYSICAL PARAMETERS THROUGH AIRBORNE AND FIELD HYPERSPECTRAL REMOTE SENSING

The potential of airborne hyperspectral remote sensing in crop monitoring and estimation of various biophysical parameters was examined in this study. A field experiment, consisting of four weed control strategies (no weed control, broadleaf control, grass control, and full weed control) as the main plot effect, factorially combined with three nitrogen (N) fertilization rates (60, 120, and 250 N kg ha–1), and replicated four times, was conducted. Hyperspectral data in 72 narrow wavebands (409 to 947 nm) from a Compact Airborne Spectrographic Imager (CASI) sensor were acquired 30 days after planting, at tasseling, and at the fully mature stage. In addition, measurements were made concurrently on various crop physiological parameters: leaf greenness (SPAD readings), leaf area index (LAI), plant height, leaf nitrogen content, leaf chlorophyll content, and associated factors such as soil moisture. Regression models were generated to estimate crop biophysical parameters and yield, in terms of reflectance at one or more wavebands, using the maximum r2 improvement criterion. The models that best represented the data had five wavebands as independent variables. Coefficients of determination (r2) were generally greater than 0.9, when based on the spectral data taken at the tasseling stage. Results were improved when normalized difference vegetation indices (NDVI) were used rather than the five–waveband reflectance values. The wavebands at 701 nm and 839 nm were the most prevalent in the NDVI–based models.

Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn

A compact airborne spectrographic imager (CASI) was used to obtain images over a field that had been set up to study the effects of various nitrogen application rates and weed control on corn (Zea mays). The objective was to determine to what extent the reflectances obtained in the 72 visible and near-infrared (NIR) wavebands (from 409 to 947 nm) might be related to differences associated with combinations of weed control (none, full, grasses only or broadleafs only) and nitrogen application rate (60, 120 or 250 kg/ha). Plots were arranged in split-plot experiment in completely randomized design at the McGill University Research Farm on Macdonald Campus, Ste Anne de Bellevue, Que., Canada. Weeding treatments were assigned to the main-plot units, and nitrogen rates to the sub-plot units. Three flights were made during the growing season. Data were analyzed for each flight and each band separately, then regrouped into series of neighboring bands yielding identical analyses with respect to the significance of the main effects and interactions on reflectance. The results indicate that the reflectance of corn is significantly influenced (α=0.05) at certain wavelengths by the presence of weeds, the nitrogen rates and their interaction. The influence of weeds was most easily observed in the data from the second flight (August 5, 2000), about 9 weeks after planting. The nitrogen effect was detectable in all the three flights. Differences in response due to nitrogen stress were most evident at 498 nm and in the band at 671 nm. In these bands, differences due to nitrogen levels were observed at all growth stages, and the presence of weeds had no interactive effect. Differences in other regions, whether related to nitrogen, weeds or the combination of the two, appeared to be dependent on the growth stage. Furthermore, results comparable to those of the hyperspectral sensor were obtained when a multispectral sensor was simulated, indicating little advantage of using the former.

APPLICATION OF DECISION TREE TECHNOLOGY FOR IMAGE CLASSIFICATION USING REMOTE SENSING DATA

Abstract Hyperspectral images of plots, cropped with silage or grain corn and cultivated with conventional tillage, reduced tillage, or no till, were classified using the classification and regression tree (C&RT) approach, an innovative intelligent computational algorithm in data mining. Each tillage/cropping combination was replicated three times, for a total of 18 plots. Five hyperspectral reflectance measurements per plot were taken randomly to obtain a total of 90 measurements. Images were taken on June 30, August 5, and August 25, 2000 to reflect three stages of crop development. Each measurement consisted of reflectances in 71 wave bands ranging from 400 to 950 nm. C&RT models were developed separately for the three observation dates, using the 71 reflectances as inputs to classify the image according to: (a) tillage practice, (b) residue level, (c) cropping practices, (d) tillage/cropping (residue) combination. C&RT models could generally distinguish tillage practices with a classification accuracy of 0.89 and residue levels with a classification accuracy of 0.98.

Rhizospheric effects of alfalfa on biotransformation of polychlorinated biphenyls in a contaminated soil augmented with Sinorhizobium meliloti

Abstract The effects of plant–microbe–soil interactions on the biotransformation of polychlorinated biphenyls (PCBs) in a rhizosphere soil were investigated. Containers packed with 350 g of a soil contaminated with Aroclor 1242, 1248, 1254 and 1260, were planted with alfalfa ( Medicago sativa L.) and augmented with its symbiotic N 2 -fixing host rhizobium ( Sinorhizobium meliloti , strain A-025). The four treatment setups comprised a factorial combination of the presence/absence of alfalfa with the non-inoculation/inoculation of soil with S. meliloti in a completely randomized design with two replicates. Up to 44 days after planting, when the alfalfa was not fully developed, alfalfa and S. meliloti together were the most effective in PCB transformation/depletion, whereas alfalfa only was the least effective. However, by the last day of the experimental period (Day 270), when alfalfa growth was robust and full, alfalfa alone was the most effective, whereas S. meliloti alone was the least. In rhizobium-inoculated soil, soil hardness increased, soil moisture contents decreased, and both plant growth and yield were lowered, compared to non-inoculated soil. The depletion, loss or change in PCB levels may be attributed to either direct or indirect biotransformation, biotranslocation and adsorption of PCBs due to the presence of alfalfa and/or rhizobial inoculation. Either possibility underscores the possibility of using plant-rhizobacterial associations to phytoremediate soils contaminated with PCBs.

Using causal loop diagrams for the initialization of stakeholder engagement in soil salinity management in agricultural watersheds in developing countries: A case study in the Rechna Doab watershed, Pakistan

Over the course of the last twenty years, participatory modeling has increasingly been advocated as an integral component of integrated, adaptive, and collaborative water resources management. However, issues of high cost, time, and expertise are significant hurdles to the widespread adoption of participatory modeling in many developing countries. In this study, a step-wise method to initialize the involvement of key stakeholders in the development of qualitative system dynamics models (i.e. causal loop diagrams) is presented. The proposed approach is designed to overcome the challenges of low expertise, time and financial resources that have hampered previous participatory modeling efforts in developing countries. The methodological framework was applied in a case study of soil salinity management in the Rechna Doab region of Pakistan, with a focus on the application of qualitative modeling through stakeholder-built causal loop diagrams to address soil salinity problems in the basin. Individual causal loop diagrams were developed by key stakeholder groups, following which an overall group causal loop diagram of the entire system was built based on the individual causal loop diagrams to form a holistic qualitative model of the whole system. The case study demonstrates the usefulness of the proposed approach, based on using causal loop diagrams in initiating stakeholder involvement in the participatory model building process. In addition, the results point to social-economic aspects of soil salinity that have not been considered by other modeling studies to date.

Performance of Multivariate Adaptive Regression Splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data / Performances de régressions par splines multiples et adaptives (MARS) pour la prévision d'écoulement au sein de micro-bassins versants Himalayens d'altitudes intermédiaires avec peu de données

Abstract Steep topography and land-use transformations in Himalayan watersheds have a major impact on hydrological characteristics and flow regimes, and greatly affect the perenniality and sustainability of water resources in the region. To identify the appropriate conservation measures in a watershed properly, and, in particular, to augment flow during lean periods, accurate estimation of streamflow is essential. Due to the complexity of rainfall—runoff relationships in hilly watersheds and non-availability of reliable data, process-based models have limited applicability. In this study, data-driven models, based upon the Multiple Adaptive Regression Splines (MARS) technique, were employed to predict streamflow (surface runoff, baseflow and total runoff) in three mid-Himalayan micro-watersheds. In addition, the effect of length of historical records on the performance of MARS models was critically evaluated. Though acceptable MARS models could be developed with a 2-year data set, their performance improved considerably with a 3-year data set. Various indicators of model performance, such as correlation coefficient, average deviation, average absolute deviation and modelling efficiency, showed significant improvement for simulation of surface runoff, baseflow and total flow. To further analyse the versatility and general applicability of the MARS approach, 2-year data sets were used to develop the model and test it on a third-year data set to assess its performance. The models simulated the surface runoff, baseflow and total flow reasonably well and can be reliably applied in ungauged small watersheds under identical agro-climatic settings.